标题： 区分神经元的苹果和橘子：神经组织模拟的分析性能建模 显示英文标题

标题： Telling neuronal apples from oranges: analytical performance modeling of neural tissue simulations

摘要： 计算建模和仿真已成为更好地理解大脑结构以及解析其组成部分因果关系的重要工具。 大脑中生化和生物物理过程及结构的广泛性导致了各种模型抽象和专用工具的发展，通常需要高性能计算资源以及时执行。 到目前为止所缺失的是对计算内核复杂性的深入分析，这阻碍了系统地识别算法和硬件及其组合中的瓶颈。 如果要在新兴计算机上实现全脑模型，模型和仿真引擎必须针对内在的硬件权衡进行仔细的协同设计。 首次，我们基于分析性能建模进行了系统探索。 我们的分析基于三个体外模型，这些模型被选为最广泛使用的建模抽象的代表性例子。 我们发现，突触形式，即电流或电导为基础的表示方式，而不是形态细节的层次，是决定体外模型内存带宽饱和和共享内存扩展特性的最重要因素。 尽管通用计算到目前为止一直在能够提供高性能，但我们发现，对于所有类型的抽象，随着要模拟的神经元数量增加，网络延迟和内存带宽将变得严重瓶颈。 通过适应和扩展一种性能建模方法，我们首次对脑组织仿真的性能格局进行了表征，使我们能够确定当前最先进的体外模型中的瓶颈，并对未来硬件和软件需求做出预测。 显示英文摘要

摘要： Computational modeling and simulation have become essential tools in the quest to better understand the brain's makeup and to decipher the causal interrelations of its components. The breadth of biochemical and biophysical processes and structures in the brain has led to the development of a large variety of model abstractions and specialized tools, often times requiring high performance computing resource for their timely execution. What has been missing so far was an in-depth analysis of the complexity of the computational kernels, hindering a systematic approach to identifying bottlenecks of algorithms and hardware, and their combinations. If whole brain models are to be achieved on emerging computer generations, models and simulation engines will have to be carefully co-designed for the intrinsic hardware tradeoffs. For the first time, we present a systematic exploration based on analytic performance modeling. We base our analysis on three in silico models, chosen as representative examples of the most widely employed modeling abstractions. We identify that the synaptic formalism, i.e. current or conductance based representations, and not the level of morphological detail, is the most significant factor in determining the properties of memory bandwidth saturation and shared-memory scaling of in silico models. Even though general purpose computing has, until now, largely been able to deliver high performance, we find that for all types of abstractions, network latency and memory bandwidth will become severe bottlenecks as the number of neurons to be simulated grows. By adapting and extending a performance modeling approach, we deliver a first characterization of the performance landscape of brain tissue simulations, allowing us to pinpoint current bottlenecks in state-of-the-art in silico models, and make projections for future hardware and software requirements.